Field
The described embodiments relate to techniques for communicating information among electronic devices. In particular, the described embodiments relate to techniques for detecting anomalies based on access-point performance indicators.
Related Art
Many electronic devices are capable of wirelessly communicating with other electronic devices. In particular, these electronic devices can include a networking subsystem that implements a network interface for: a cellular network (UMTS, LTE, etc.), a wireless local area network (e.g., a wireless network such as described in the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard or Bluetooth from the Bluetooth Special Interest Group of Kirkland, Wash.), and/or another type of wireless network.
For example, many electronic devices communicate with each other via wireless local area networks (WLANs) using an IEEE 802.11-compatible communication protocol (which are sometimes collectively referred to as ‘Wi-Fi’). In a typical deployment, a Wi-Fi-based WLAN includes one or more access points (or basic service sets or BSSs) that communicate wirelessly with each other and with other electronic devices using Wi-Fi, and that provide access to another network (such as the Internet) via IEEE 802.3 (which is sometimes referred to as ‘Ethernet’).
Wi-Fi has emerged as one of the cornerstone technologies of the mobile Internet, and the scale of Wi-Fi networks continues to increase. In the near future, carrier-class Wi-Fi networks are expected to contain several hundred thousand access points.
While large-scale Wi-Fi networks are popular because of their reduced cost, and increased coverage, and capacity, managing and maintaining such large networks can be challenging. One approach for addressing this challenge is anomaly detection, which refers to techniques of finding pattern-breaking data points that deviate from or do not conform with their expected values. As in other complex systems, anomalous samples of a certain key performance indicators for access points (such as received signal strength, client counts, session length, traffic, etc.) in a Wi-Fi network can indicate significant functional issues of the network.
However, anomaly detection techniques are usually plagued by false positives or false alarms. For example, given the dynamic and ad-hoc nature of Wi-Fi networks, many anomaly detection techniques cannot accurately detect anomalies. The incorrect anomaly detections can result in significant expense and reduced communication performance in large Wi-Fi networks, which can be frustrating to operators and can degrade the user experience of users of these networks.